Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations19
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory198.9 B

Variable types

Text7
Numeric12
DateTime1
Categorical4

Alerts

acousticness is highly overall correlated with danceability and 1 other fieldsHigh correlation
danceability is highly overall correlated with acousticness and 4 other fieldsHigh correlation
duration_ms is highly overall correlated with valenceHigh correlation
energy is highly overall correlated with loudnessHigh correlation
genero_clean is highly overall correlated with danceability and 2 other fieldsHigh correlation
liveness is highly overall correlated with acousticnessHigh correlation
loudness is highly overall correlated with danceability and 2 other fieldsHigh correlation
playlist_genre is highly overall correlated with danceability and 2 other fieldsHigh correlation
playlist_subgenre is highly overall correlated with genero_clean and 2 other fieldsHigh correlation
speechiness is highly overall correlated with playlist_subgenre and 1 other fieldsHigh correlation
tempo is highly overall correlated with danceability and 2 other fieldsHigh correlation
valence is highly overall correlated with duration_msHigh correlation
track_id has unique valuesUnique
track_name has unique valuesUnique
energy has unique valuesUnique
loudness has unique valuesUnique
speechiness has unique valuesUnique
acousticness has unique valuesUnique
liveness has unique valuesUnique
tempo has unique valuesUnique
duration_ms has unique valuesUnique
key has 2 (10.5%) zerosZeros
instrumentalness has 3 (15.8%) zerosZeros

Reproduction

Analysis started2024-09-20 00:31:17.108455
Analysis finished2024-09-20 00:31:33.240454
Duration16.13 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

track_id
Text

UNIQUE 

Distinct19
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size284.0 B
2024-09-19T18:31:33.397980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters418
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)100.0%

Sample

1st row6nTiIhLmQ3FWhvrGafw2zj
2nd row3CRDbSIZ4r5MsZ0YwxuEkn
3rd row01vv2AjxgP4uUyb8waYO5Y
4th row7i9763l5SSfOnqZ35VOcfy
5th row4EchqUKQ3qAQuRNKmeIpnf
ValueCountFrequency (%)
6ntiihlmq3fwhvrgafw2zj 1
 
5.3%
3crdbsiz4r5msz0ywxuekn 1
 
5.3%
01vv2ajxgp4uuyb8wayo5y 1
 
5.3%
7i9763l5ssfonqz35vocfy 1
 
5.3%
4echqukq3qaqurnkmeipnf 1
 
5.3%
5ohxk2do5cohf1krpopign 1
 
5.3%
5zlcyar6ti5ueoipgl41xh 1
 
5.3%
086mys9r57yslbjpu0tgk9 1
 
5.3%
6ggpso9iwdxajumnrlpam5 1
 
5.3%
5t1sjqecaltwjcfgl50aqt 1
 
5.3%
Other values (9) 9
47.4%
2024-09-19T18:31:33.744241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 18
 
4.3%
w 13
 
3.1%
6 11
 
2.6%
a 11
 
2.6%
O 11
 
2.6%
Z 10
 
2.4%
A 10
 
2.4%
y 10
 
2.4%
h 10
 
2.4%
n 9
 
2.2%
Other values (52) 305
73.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 418
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 18
 
4.3%
w 13
 
3.1%
6 11
 
2.6%
a 11
 
2.6%
O 11
 
2.6%
Z 10
 
2.4%
A 10
 
2.4%
y 10
 
2.4%
h 10
 
2.4%
n 9
 
2.2%
Other values (52) 305
73.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 418
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 18
 
4.3%
w 13
 
3.1%
6 11
 
2.6%
a 11
 
2.6%
O 11
 
2.6%
Z 10
 
2.4%
A 10
 
2.4%
y 10
 
2.4%
h 10
 
2.4%
n 9
 
2.2%
Other values (52) 305
73.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 418
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 18
 
4.3%
w 13
 
3.1%
6 11
 
2.6%
a 11
 
2.6%
O 11
 
2.6%
Z 10
 
2.4%
A 10
 
2.4%
y 10
 
2.4%
h 10
 
2.4%
n 9
 
2.2%
Other values (52) 305
73.0%

track_name
Text

UNIQUE 

Distinct19
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size284.0 B
2024-09-19T18:31:33.938818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length40
Median length12
Mean length12.578947
Min length5

Characters and Unicode

Total characters239
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)100.0%

Sample

1st rowAmerican Idiot
2nd rowStressed Out
3rd rowMorph
4th rowHeavydirtysoul
5th rowThe Kids Aren't Alright
ValueCountFrequency (%)
the 3
 
7.3%
me 2
 
4.9%
out 2
 
4.9%
american 1
 
2.4%
idiot 1
 
2.4%
morph 1
 
2.4%
stressed 1
 
2.4%
kids 1
 
2.4%
heavydirtysoul 1
 
2.4%
alright 1
 
2.4%
Other values (27) 27
65.9%
2024-09-19T18:31:34.294941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 24
 
10.0%
22
 
9.2%
i 14
 
5.9%
a 13
 
5.4%
n 13
 
5.4%
r 12
 
5.0%
o 10
 
4.2%
h 10
 
4.2%
l 9
 
3.8%
d 9
 
3.8%
Other values (30) 103
43.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 239
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 24
 
10.0%
22
 
9.2%
i 14
 
5.9%
a 13
 
5.4%
n 13
 
5.4%
r 12
 
5.0%
o 10
 
4.2%
h 10
 
4.2%
l 9
 
3.8%
d 9
 
3.8%
Other values (30) 103
43.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 239
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 24
 
10.0%
22
 
9.2%
i 14
 
5.9%
a 13
 
5.4%
n 13
 
5.4%
r 12
 
5.0%
o 10
 
4.2%
h 10
 
4.2%
l 9
 
3.8%
d 9
 
3.8%
Other values (30) 103
43.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 239
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 24
 
10.0%
22
 
9.2%
i 14
 
5.9%
a 13
 
5.4%
n 13
 
5.4%
r 12
 
5.0%
o 10
 
4.2%
h 10
 
4.2%
l 9
 
3.8%
d 9
 
3.8%
Other values (30) 103
43.1%
Distinct16
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Memory size284.0 B
2024-09-19T18:31:34.475481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length21
Median length17
Mean length13.894737
Min length6

Characters and Unicode

Total characters264
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)73.7%

Sample

1st rowGreen Day
2nd rowTwenty One Pilots
3rd rowTwenty One Pilots
4th rowTwenty One Pilots
5th rowThe Offspring
ValueCountFrequency (%)
the 6
 
12.5%
twenty 3
 
6.2%
one 3
 
6.2%
pilots 3
 
6.2%
bring 2
 
4.2%
me 2
 
4.2%
horizon 2
 
4.2%
offspring 1
 
2.1%
green 1
 
2.1%
day 1
 
2.1%
Other values (24) 24
50.0%
2024-09-19T18:31:34.843647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 29
 
11.0%
29
 
11.0%
i 18
 
6.8%
n 17
 
6.4%
r 15
 
5.7%
o 14
 
5.3%
l 13
 
4.9%
T 11
 
4.2%
t 11
 
4.2%
s 10
 
3.8%
Other values (31) 97
36.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 264
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 29
 
11.0%
29
 
11.0%
i 18
 
6.8%
n 17
 
6.4%
r 15
 
5.7%
o 14
 
5.3%
l 13
 
4.9%
T 11
 
4.2%
t 11
 
4.2%
s 10
 
3.8%
Other values (31) 97
36.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 264
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 29
 
11.0%
29
 
11.0%
i 18
 
6.8%
n 17
 
6.4%
r 15
 
5.7%
o 14
 
5.3%
l 13
 
4.9%
T 11
 
4.2%
t 11
 
4.2%
s 10
 
3.8%
Other values (31) 97
36.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 264
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 29
 
11.0%
29
 
11.0%
i 18
 
6.8%
n 17
 
6.4%
r 15
 
5.7%
o 14
 
5.3%
l 13
 
4.9%
T 11
 
4.2%
t 11
 
4.2%
s 10
 
3.8%
Other values (31) 97
36.7%

track_popularity
Real number (ℝ)

Distinct17
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54
Minimum2
Maximum83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2024-09-19T18:31:34.994138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q148.5
median58
Q374.5
95-th percentile80.3
Maximum83
Range81
Interquartile range (IQR)26

Descriptive statistics

Standard deviation25.892942
Coefficient of variation (CV)0.47949893
Kurtosis-0.058662655
Mean54
Median Absolute Deviation (MAD)15
Skewness-1.0088352
Sum1026
Variance670.44444
MonotonicityNot monotonic
2024-09-19T18:31:35.147193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
76 2
 
10.5%
2 2
 
10.5%
78 1
 
5.3%
70 1
 
5.3%
83 1
 
5.3%
80 1
 
5.3%
55 1
 
5.3%
23 1
 
5.3%
71 1
 
5.3%
53 1
 
5.3%
Other values (7) 7
36.8%
ValueCountFrequency (%)
2 2
10.5%
14 1
5.3%
23 1
5.3%
47 1
5.3%
50 1
5.3%
53 1
5.3%
54 1
5.3%
55 1
5.3%
58 1
5.3%
61 1
5.3%
ValueCountFrequency (%)
83 1
5.3%
80 1
5.3%
78 1
5.3%
76 2
10.5%
73 1
5.3%
71 1
5.3%
70 1
5.3%
61 1
5.3%
58 1
5.3%
55 1
5.3%
Distinct18
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size284.0 B
2024-09-19T18:31:35.373746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters418
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)89.5%

Sample

1st row5dN7F9DV0Qg1XRdIgW8rke
2nd row3cQO7jp5S9qLBoIVtbkSM1
3rd row621cXqrTSSJi1WqDMSLmbL
4th row3cQO7jp5S9qLBoIVtbkSM1
5th row2RNTBrSO8U8XjjEj9RVvZ5
ValueCountFrequency (%)
3cqo7jp5s9qlboivtbksm1 2
 
10.5%
5dn7f9dv0qg1xrdigw8rke 1
 
5.3%
621cxqrtssji1wqdmslmbl 1
 
5.3%
2rntbrso8u8xjjej9rvvz5 1
 
5.3%
1fzkim3jvdcxtchxdo5jov 1
 
5.3%
7mxf1fnld12k1e5mpkmdkg 1
 
5.3%
78bpiziexqii9qztvnflqu 1
 
5.3%
4zkvdtygr99rwfrs0fno4d 1
 
5.3%
2tsteamvl7vkfgaewysgyp 1
 
5.3%
2cutwbzsddkgequlgpzxyn 1
 
5.3%
Other values (8) 8
42.1%
2024-09-19T18:31:35.724289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 14
 
3.3%
j 11
 
2.6%
S 11
 
2.6%
O 10
 
2.4%
d 10
 
2.4%
V 10
 
2.4%
k 10
 
2.4%
1 10
 
2.4%
D 9
 
2.2%
t 9
 
2.2%
Other values (52) 314
75.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 418
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 14
 
3.3%
j 11
 
2.6%
S 11
 
2.6%
O 10
 
2.4%
d 10
 
2.4%
V 10
 
2.4%
k 10
 
2.4%
1 10
 
2.4%
D 9
 
2.2%
t 9
 
2.2%
Other values (52) 314
75.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 418
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 14
 
3.3%
j 11
 
2.6%
S 11
 
2.6%
O 10
 
2.4%
d 10
 
2.4%
V 10
 
2.4%
k 10
 
2.4%
1 10
 
2.4%
D 9
 
2.2%
t 9
 
2.2%
Other values (52) 314
75.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 418
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 14
 
3.3%
j 11
 
2.6%
S 11
 
2.6%
O 10
 
2.4%
d 10
 
2.4%
V 10
 
2.4%
k 10
 
2.4%
1 10
 
2.4%
D 9
 
2.2%
t 9
 
2.2%
Other values (52) 314
75.1%
Distinct18
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size284.0 B
2024-09-19T18:31:35.934364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length45
Median length14
Mean length12.473684
Min length2

Characters and Unicode

Total characters237
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)89.5%

Sample

1st rowAmerican Idiot
2nd rowBlurryface
3rd rowTrench
4th rowBlurryface
5th rowAmericana
ValueCountFrequency (%)
blurryface 2
 
5.3%
down 2
 
5.3%
2
 
5.3%
track 2
 
5.3%
the 2
 
5.3%
harry 1
 
2.6%
styles 1
 
2.6%
american 1
 
2.6%
idiot 1
 
2.6%
am 1
 
2.6%
Other values (23) 23
60.5%
2024-09-19T18:31:36.259062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 21
 
8.9%
r 20
 
8.4%
a 20
 
8.4%
19
 
8.0%
n 14
 
5.9%
i 12
 
5.1%
c 10
 
4.2%
o 8
 
3.4%
y 7
 
3.0%
T 7
 
3.0%
Other values (30) 99
41.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 237
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 21
 
8.9%
r 20
 
8.4%
a 20
 
8.4%
19
 
8.0%
n 14
 
5.9%
i 12
 
5.1%
c 10
 
4.2%
o 8
 
3.4%
y 7
 
3.0%
T 7
 
3.0%
Other values (30) 99
41.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 237
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 21
 
8.9%
r 20
 
8.4%
a 20
 
8.4%
19
 
8.0%
n 14
 
5.9%
i 12
 
5.1%
c 10
 
4.2%
o 8
 
3.4%
y 7
 
3.0%
T 7
 
3.0%
Other values (30) 99
41.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 237
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 21
 
8.9%
r 20
 
8.4%
a 20
 
8.4%
19
 
8.0%
n 14
 
5.9%
i 12
 
5.1%
c 10
 
4.2%
o 8
 
3.4%
y 7
 
3.0%
T 7
 
3.0%
Other values (30) 99
41.8%
Distinct18
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size284.0 B
Minimum1998-11-16 00:00:00
Maximum2020-01-10 00:00:00
2024-09-19T18:31:36.382550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:36.518574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
Distinct10
Distinct (%)52.6%
Missing0
Missing (%)0.0%
Memory size284.0 B
2024-09-19T18:31:36.691168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length92
Median length45
Mean length18.157895
Min length9

Characters and Unicode

Total characters345
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)42.1%

Sample

1st rowDr. Q's Prescription Playlist💊
2nd rowBALLARE - رقص
3rd rowElectropop
4th row②⓪①⑨ mixed
5th rowSNZB PERMANENT WAVE
ValueCountFrequency (%)
rock 14
21.5%
hard 10
15.4%
7
 
10.8%
permanent 4
 
6.2%
wave 4
 
6.2%
pop 2
 
3.1%
رقص 1
 
1.5%
dr 1
 
1.5%
q's 1
 
1.5%
prescription 1
 
1.5%
Other values (20) 20
30.8%
2024-09-19T18:31:36.992256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
46
 
13.3%
o 27
 
7.8%
r 25
 
7.2%
a 20
 
5.8%
e 19
 
5.5%
k 17
 
4.9%
R 16
 
4.6%
d 16
 
4.6%
c 16
 
4.6%
n 14
 
4.1%
Other values (45) 129
37.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 345
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
46
 
13.3%
o 27
 
7.8%
r 25
 
7.2%
a 20
 
5.8%
e 19
 
5.5%
k 17
 
4.9%
R 16
 
4.6%
d 16
 
4.6%
c 16
 
4.6%
n 14
 
4.1%
Other values (45) 129
37.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 345
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
46
 
13.3%
o 27
 
7.8%
r 25
 
7.2%
a 20
 
5.8%
e 19
 
5.5%
k 17
 
4.9%
R 16
 
4.6%
d 16
 
4.6%
c 16
 
4.6%
n 14
 
4.1%
Other values (45) 129
37.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 345
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
46
 
13.3%
o 27
 
7.8%
r 25
 
7.2%
a 20
 
5.8%
e 19
 
5.5%
k 17
 
4.9%
R 16
 
4.6%
d 16
 
4.6%
c 16
 
4.6%
n 14
 
4.1%
Other values (45) 129
37.4%
Distinct10
Distinct (%)52.6%
Missing0
Missing (%)0.0%
Memory size284.0 B
2024-09-19T18:31:37.187799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters418
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)42.1%

Sample

1st row6jAPdgY9XmxC9cgkXAVmVv
2nd row1CMvQ4Yr5DlYvYzI0Vc2UE
3rd row2Z5cPJ6Z4EVZAfF08amjvL
4th row2bOjjgN1S3Gqd8vSMyafvJ
5th row6CgjYkPIWTxJi8RtPcki02
ValueCountFrequency (%)
37i9dqzf1dwwjomj7nrx0c 9
47.4%
3ufygoayrp71xs6t6y8bh2 2
 
10.5%
1cmvq4yr5dlyvyzi0vc2ue 1
 
5.3%
6japdgy9xmxc9cgkxavmvv 1
 
5.3%
2z5cpj6z4evzaff08amjvl 1
 
5.3%
2bojjgn1s3gqd8vsmyafvj 1
 
5.3%
3e6gypyrtbab8bwgshct5j 1
 
5.3%
6cgjykpiwtxji8rtpcki02 1
 
5.3%
1vnvybdqov5tczanxyferl 1
 
5.3%
4tg1lzmd9hfvz9e1bk6gnu 1
 
5.3%
2024-09-19T18:31:37.509434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
J 21
 
5.0%
7 20
 
4.8%
W 20
 
4.8%
1 16
 
3.8%
Z 14
 
3.3%
C 14
 
3.3%
x 13
 
3.1%
3 13
 
3.1%
9 13
 
3.1%
F 13
 
3.1%
Other values (48) 261
62.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 418
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
J 21
 
5.0%
7 20
 
4.8%
W 20
 
4.8%
1 16
 
3.8%
Z 14
 
3.3%
C 14
 
3.3%
x 13
 
3.1%
3 13
 
3.1%
9 13
 
3.1%
F 13
 
3.1%
Other values (48) 261
62.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 418
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
J 21
 
5.0%
7 20
 
4.8%
W 20
 
4.8%
1 16
 
3.8%
Z 14
 
3.3%
C 14
 
3.3%
x 13
 
3.1%
3 13
 
3.1%
9 13
 
3.1%
F 13
 
3.1%
Other values (48) 261
62.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 418
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
J 21
 
5.0%
7 20
 
4.8%
W 20
 
4.8%
1 16
 
3.8%
Z 14
 
3.3%
C 14
 
3.3%
x 13
 
3.1%
3 13
 
3.1%
9 13
 
3.1%
F 13
 
3.1%
Other values (48) 261
62.4%

playlist_genre
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size284.0 B
rock
16 
pop

Length

Max length4
Median length4
Mean length3.8421053
Min length3

Characters and Unicode

Total characters73
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpop
2nd rowpop
3rd rowpop
4th rowrock
5th rowrock

Common Values

ValueCountFrequency (%)
rock 16
84.2%
pop 3
 
15.8%

Length

2024-09-19T18:31:37.646004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-19T18:31:37.763528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
rock 16
84.2%
pop 3
 
15.8%

Most occurring characters

ValueCountFrequency (%)
o 19
26.0%
r 16
21.9%
c 16
21.9%
k 16
21.9%
p 6
 
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 19
26.0%
r 16
21.9%
c 16
21.9%
k 16
21.9%
p 6
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 19
26.0%
r 16
21.9%
c 16
21.9%
k 16
21.9%
p 6
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 19
26.0%
r 16
21.9%
c 16
21.9%
k 16
21.9%
p 6
 
8.2%

playlist_subgenre
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Memory size284.0 B
hard rock
10 
permanent wave
post-teen pop
electropop
 
1

Length

Max length14
Median length9
Mean length11.052632
Min length9

Characters and Unicode

Total characters210
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)5.3%

Sample

1st rowpost-teen pop
2nd rowpost-teen pop
3rd rowelectropop
4th rowpermanent wave
5th rowpermanent wave

Common Values

ValueCountFrequency (%)
hard rock 10
52.6%
permanent wave 6
31.6%
post-teen pop 2
 
10.5%
electropop 1
 
5.3%

Length

2024-09-19T18:31:37.898057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-19T18:31:38.047119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
hard 10
27.0%
rock 10
27.0%
permanent 6
16.2%
wave 6
16.2%
post-teen 2
 
5.4%
pop 2
 
5.4%
electropop 1
 
2.7%

Most occurring characters

ValueCountFrequency (%)
r 27
12.9%
e 24
11.4%
a 22
10.5%
18
8.6%
o 16
 
7.6%
p 14
 
6.7%
n 14
 
6.7%
t 11
 
5.2%
c 11
 
5.2%
h 10
 
4.8%
Other values (8) 43
20.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 210
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 27
12.9%
e 24
11.4%
a 22
10.5%
18
8.6%
o 16
 
7.6%
p 14
 
6.7%
n 14
 
6.7%
t 11
 
5.2%
c 11
 
5.2%
h 10
 
4.8%
Other values (8) 43
20.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 210
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 27
12.9%
e 24
11.4%
a 22
10.5%
18
8.6%
o 16
 
7.6%
p 14
 
6.7%
n 14
 
6.7%
t 11
 
5.2%
c 11
 
5.2%
h 10
 
4.8%
Other values (8) 43
20.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 210
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 27
12.9%
e 24
11.4%
a 22
10.5%
18
8.6%
o 16
 
7.6%
p 14
 
6.7%
n 14
 
6.7%
t 11
 
5.2%
c 11
 
5.2%
h 10
 
4.8%
Other values (8) 43
20.5%

danceability
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50010526
Minimum0.185
Maximum0.734
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2024-09-19T18:31:38.185662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.185
5-th percentile0.284
Q10.4525
median0.516
Q30.553
95-th percentile0.734
Maximum0.734
Range0.549
Interquartile range (IQR)0.1005

Descriptive statistics

Standard deviation0.14319447
Coefficient of variation (CV)0.28632865
Kurtosis0.31732427
Mean0.50010526
Median Absolute Deviation (MAD)0.06
Skewness-0.33896236
Sum9.502
Variance0.020504655
MonotonicityNot monotonic
2024-09-19T18:31:38.301658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.734 2
 
10.5%
0.38 1
 
5.3%
0.613 1
 
5.3%
0.523 1
 
5.3%
0.516 1
 
5.3%
0.53 1
 
5.3%
0.691 1
 
5.3%
0.497 1
 
5.3%
0.491 1
 
5.3%
0.456 1
 
5.3%
Other values (8) 8
42.1%
ValueCountFrequency (%)
0.185 1
5.3%
0.295 1
5.3%
0.296 1
5.3%
0.38 1
5.3%
0.449 1
5.3%
0.456 1
5.3%
0.48 1
5.3%
0.491 1
5.3%
0.497 1
5.3%
0.516 1
5.3%
ValueCountFrequency (%)
0.734 2
10.5%
0.691 1
5.3%
0.613 1
5.3%
0.558 1
5.3%
0.548 1
5.3%
0.53 1
5.3%
0.526 1
5.3%
0.523 1
5.3%
0.516 1
5.3%
0.497 1
5.3%

energy
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct19
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.82631579
Minimum0.595
Maximum0.988
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2024-09-19T18:31:38.421766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.595
5-th percentile0.6058
Q10.715
median0.873
Q30.927
95-th percentile0.9817
Maximum0.988
Range0.393
Interquartile range (IQR)0.212

Descriptive statistics

Standard deviation0.13704016
Coefficient of variation (CV)0.16584478
Kurtosis-1.2122159
Mean0.82631579
Median Absolute Deviation (MAD)0.084
Skewness-0.57645904
Sum15.7
Variance0.018780006
MonotonicityNot monotonic
2024-09-19T18:31:38.561338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.988 1
 
5.3%
0.637 1
 
5.3%
0.607 1
 
5.3%
0.873 1
 
5.3%
0.943 1
 
5.3%
0.595 1
 
5.3%
0.751 1
 
5.3%
0.631 1
 
5.3%
0.923 1
 
5.3%
0.981 1
 
5.3%
Other values (9) 9
47.4%
ValueCountFrequency (%)
0.595 1
5.3%
0.607 1
5.3%
0.631 1
5.3%
0.637 1
5.3%
0.679 1
5.3%
0.751 1
5.3%
0.789 1
5.3%
0.793 1
5.3%
0.861 1
5.3%
0.873 1
5.3%
ValueCountFrequency (%)
0.988 1
5.3%
0.981 1
5.3%
0.973 1
5.3%
0.943 1
5.3%
0.93 1
5.3%
0.924 1
5.3%
0.923 1
5.3%
0.912 1
5.3%
0.91 1
5.3%
0.873 1
5.3%

key
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)47.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2105263
Minimum0
Maximum11
Zeros2
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size284.0 B
2024-09-19T18:31:38.684899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q36.5
95-th percentile8.3
Maximum11
Range11
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.0836297
Coefficient of variation (CV)0.73236205
Kurtosis-0.48150506
Mean4.2105263
Median Absolute Deviation (MAD)2
Skewness0.31281936
Sum80
Variance9.5087719
MonotonicityNot monotonic
2024-09-19T18:31:38.803464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 4
21.1%
5 4
21.1%
7 3
15.8%
4 2
10.5%
0 2
10.5%
8 1
 
5.3%
2 1
 
5.3%
6 1
 
5.3%
11 1
 
5.3%
ValueCountFrequency (%)
0 2
10.5%
1 4
21.1%
2 1
 
5.3%
4 2
10.5%
5 4
21.1%
6 1
 
5.3%
7 3
15.8%
8 1
 
5.3%
11 1
 
5.3%
ValueCountFrequency (%)
11 1
 
5.3%
8 1
 
5.3%
7 3
15.8%
6 1
 
5.3%
5 4
21.1%
4 2
10.5%
2 1
 
5.3%
1 4
21.1%
0 2
10.5%

loudness
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct19
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.1134211
Minimum-7.342
Maximum-2.042
Zeros0
Zeros (%)0.0%
Negative19
Negative (%)100.0%
Memory size284.0 B
2024-09-19T18:31:38.965988image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-7.342
5-th percentile-7.2583
Q1-6.427
median-5.165
Q3-3.938
95-th percentile-2.9402
Maximum-2.042
Range5.3
Interquartile range (IQR)2.489

Descriptive statistics

Standard deviation1.5482566
Coefficient of variation (CV)-0.30278293
Kurtosis-0.91209442
Mean-5.1134211
Median Absolute Deviation (MAD)1.313
Skewness0.2200336
Sum-97.155
Variance2.3970986
MonotonicityNot monotonic
2024-09-19T18:31:39.107537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
-2.042 1
 
5.3%
-5.677 1
 
5.3%
-7.249 1
 
5.3%
-6.376 1
 
5.3%
-4.203 1
 
5.3%
-4.63 1
 
5.3%
-5.165 1
 
5.3%
-6.478 1
 
5.3%
-3.04 1
 
5.3%
-3.673 1
 
5.3%
Other values (9) 9
47.4%
ValueCountFrequency (%)
-7.342 1
5.3%
-7.249 1
5.3%
-6.861 1
5.3%
-6.836 1
5.3%
-6.478 1
5.3%
-6.376 1
5.3%
-6.182 1
5.3%
-5.677 1
5.3%
-5.283 1
5.3%
-5.165 1
5.3%
ValueCountFrequency (%)
-2.042 1
5.3%
-3.04 1
5.3%
-3.433 1
5.3%
-3.596 1
5.3%
-3.673 1
5.3%
-4.203 1
5.3%
-4.48 1
5.3%
-4.609 1
5.3%
-4.63 1
5.3%
-5.165 1
5.3%

mode
Categorical

Distinct2
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size284.0 B
1
12 
0

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 12
63.2%
0 7
36.8%

Length

2024-09-19T18:31:39.250613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-19T18:31:39.366647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 12
63.2%
0 7
36.8%

Most occurring characters

ValueCountFrequency (%)
1 12
63.2%
0 7
36.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 12
63.2%
0 7
36.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 12
63.2%
0 7
36.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 12
63.2%
0 7
36.8%

speechiness
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct19
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.086273684
Minimum0.0313
Maximum0.186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2024-09-19T18:31:39.473701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0313
5-th percentile0.0331
Q10.0403
median0.0645
Q30.136
95-th percentile0.1842
Maximum0.186
Range0.1547
Interquartile range (IQR)0.0957

Descriptive statistics

Standard deviation0.054312377
Coefficient of variation (CV)0.62953585
Kurtosis-1.04971
Mean0.086273684
Median Absolute Deviation (MAD)0.0308
Skewness0.6932681
Sum1.6392
Variance0.0029498343
MonotonicityNot monotonic
2024-09-19T18:31:39.601306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.0639 1
 
5.3%
0.141 1
 
5.3%
0.0806 1
 
5.3%
0.0449 1
 
5.3%
0.0337 1
 
5.3%
0.0313 1
 
5.3%
0.0432 1
 
5.3%
0.0368 1
 
5.3%
0.11 1
 
5.3%
0.131 1
 
5.3%
Other values (9) 9
47.4%
ValueCountFrequency (%)
0.0313 1
5.3%
0.0333 1
5.3%
0.0337 1
5.3%
0.0368 1
5.3%
0.0374 1
5.3%
0.0432 1
5.3%
0.0449 1
5.3%
0.0477 1
5.3%
0.0639 1
5.3%
0.0645 1
5.3%
ValueCountFrequency (%)
0.186 1
5.3%
0.184 1
5.3%
0.151 1
5.3%
0.15 1
5.3%
0.141 1
5.3%
0.131 1
5.3%
0.11 1
5.3%
0.0806 1
5.3%
0.0689 1
5.3%
0.0645 1
5.3%

acousticness
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct19
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.013368732
Minimum2.64 × 10-5
Maximum0.0747
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2024-09-19T18:31:39.729299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.64 × 10-5
5-th percentile3.369 × 10-5
Q10.0004405
median0.00397
Q30.0139
95-th percentile0.05094
Maximum0.0747
Range0.0746736
Interquartile range (IQR)0.0134595

Descriptive statistics

Standard deviation0.021016127
Coefficient of variation (CV)1.5720359
Kurtosis3.2258063
Mean0.013368732
Median Absolute Deviation (MAD)0.00383
Skewness1.9486039
Sum0.2540059
Variance0.00044167758
MonotonicityNot monotonic
2024-09-19T18:31:39.879367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2.64 × 10-51
 
5.3%
0.0462 1
 
5.3%
0.0747 1
 
5.3%
0.00397 1
 
5.3%
0.00704 1
 
5.3%
0.0275 1
 
5.3%
0.000103 1
 
5.3%
0.0483 1
 
5.3%
0.00627 1
 
5.3%
0.0116 1
 
5.3%
Other values (9) 9
47.4%
ValueCountFrequency (%)
2.64 × 10-51
5.3%
3.45 × 10-51
5.3%
0.000103 1
5.3%
0.00014 1
5.3%
0.00033 1
5.3%
0.000551 1
5.3%
0.000641 1
5.3%
0.0022 1
5.3%
0.00329 1
5.3%
0.00397 1
5.3%
ValueCountFrequency (%)
0.0747 1
5.3%
0.0483 1
5.3%
0.0462 1
5.3%
0.0275 1
5.3%
0.0162 1
5.3%
0.0116 1
5.3%
0.00704 1
5.3%
0.00627 1
5.3%
0.00491 1
5.3%
0.00397 1
5.3%

instrumentalness
Real number (ℝ)

ZEROS 

Distinct17
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0042573384
Minimum0
Maximum0.0559
Zeros3
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size284.0 B
2024-09-19T18:31:40.023429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.275 × 10-5
median5.44 × 10-5
Q30.0010405
95-th percentile0.02134
Maximum0.0559
Range0.0559
Interquartile range (IQR)0.00102775

Descriptive statistics

Standard deviation0.013121812
Coefficient of variation (CV)3.0821632
Kurtosis15.139057
Mean0.0042573384
Median Absolute Deviation (MAD)5.44 × 10-5
Skewness3.8203598
Sum0.08088943
Variance0.00017218195
MonotonicityNot monotonic
2024-09-19T18:31:40.290996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 3
15.8%
7.86 × 10-51
 
5.3%
2.29 × 10-51
 
5.3%
0.00111 1
 
5.3%
0.000577 1
 
5.3%
3.81 × 10-51
 
5.3%
0.00175 1
 
5.3%
1.13 × 10-51
 
5.3%
0.000971 1
 
5.3%
0.000191 1
 
5.3%
Other values (7) 7
36.8%
ValueCountFrequency (%)
0 3
15.8%
1.63 × 10-61
 
5.3%
1.13 × 10-51
 
5.3%
1.42 × 10-51
 
5.3%
2.29 × 10-51
 
5.3%
2.93 × 10-51
 
5.3%
3.81 × 10-51
 
5.3%
5.44 × 10-51
 
5.3%
7.86 × 10-51
 
5.3%
0.000191 1
 
5.3%
ValueCountFrequency (%)
0.0559 1
5.3%
0.0175 1
5.3%
0.00264 1
5.3%
0.00175 1
5.3%
0.00111 1
5.3%
0.000971 1
5.3%
0.000577 1
5.3%
0.000191 1
5.3%
7.86 × 10-51
5.3%
5.44 × 10-51
5.3%

liveness
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct19
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18557895
Minimum0.0579
Maximum0.731
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2024-09-19T18:31:40.409998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0579
5-th percentile0.05997
Q10.08785
median0.109
Q30.1725
95-th percentile0.4232
Maximum0.731
Range0.6731
Interquartile range (IQR)0.08465

Descriptive statistics

Standard deviation0.16832168
Coefficient of variation (CV)0.90700851
Kurtosis5.4710789
Mean0.18557895
Median Absolute Deviation (MAD)0.0488
Skewness2.2392411
Sum3.526
Variance0.02833219
MonotonicityNot monotonic
2024-09-19T18:31:40.537158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.368 1
 
5.3%
0.0602 1
 
5.3%
0.0968 1
 
5.3%
0.367 1
 
5.3%
0.0579 1
 
5.3%
0.109 1
 
5.3%
0.0926 1
 
5.3%
0.104 1
 
5.3%
0.082 1
 
5.3%
0.144 1
 
5.3%
Other values (9) 9
47.4%
ValueCountFrequency (%)
0.0579 1
5.3%
0.0602 1
5.3%
0.082 1
5.3%
0.0828 1
5.3%
0.085 1
5.3%
0.0907 1
5.3%
0.0926 1
5.3%
0.0968 1
5.3%
0.104 1
5.3%
0.109 1
5.3%
ValueCountFrequency (%)
0.731 1
5.3%
0.389 1
5.3%
0.368 1
5.3%
0.367 1
5.3%
0.18 1
5.3%
0.165 1
5.3%
0.162 1
5.3%
0.159 1
5.3%
0.144 1
5.3%
0.109 1
5.3%

valence
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44526316
Minimum0.188
Maximum0.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2024-09-19T18:31:40.663688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.188
5-th percentile0.2069
Q10.321
median0.392
Q30.5335
95-th percentile0.7721
Maximum0.8
Range0.612
Interquartile range (IQR)0.2125

Descriptive statistics

Standard deviation0.1918836
Coefficient of variation (CV)0.43094425
Kurtosis-0.60281274
Mean0.44526316
Median Absolute Deviation (MAD)0.13
Skewness0.57459872
Sum8.46
Variance0.036819316
MonotonicityNot monotonic
2024-09-19T18:31:40.787746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.366 2
 
10.5%
0.769 1
 
5.3%
0.648 1
 
5.3%
0.518 1
 
5.3%
0.766 1
 
5.3%
0.392 1
 
5.3%
0.222 1
 
5.3%
0.522 1
 
5.3%
0.8 1
 
5.3%
0.545 1
 
5.3%
Other values (8) 8
42.1%
ValueCountFrequency (%)
0.188 1
5.3%
0.209 1
5.3%
0.222 1
5.3%
0.242 1
5.3%
0.276 1
5.3%
0.366 2
10.5%
0.377 1
5.3%
0.381 1
5.3%
0.392 1
5.3%
0.416 1
5.3%
ValueCountFrequency (%)
0.8 1
5.3%
0.769 1
5.3%
0.766 1
5.3%
0.648 1
5.3%
0.545 1
5.3%
0.522 1
5.3%
0.518 1
5.3%
0.457 1
5.3%
0.416 1
5.3%
0.392 1
5.3%

tempo
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct19
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137.838
Minimum74.995
Maximum197.964
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2024-09-19T18:31:40.916334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum74.995
5-th percentile75.8986
Q1109.7895
median138.015
Q3167.666
95-th percentile196.1694
Maximum197.964
Range122.969
Interquartile range (IQR)57.8765

Descriptive statistics

Standard deviation38.730157
Coefficient of variation (CV)0.28098316
Kurtosis-0.98080846
Mean137.838
Median Absolute Deviation (MAD)31.962
Skewness-0.13625373
Sum2618.922
Variance1500.025
MonotonicityNot monotonic
2024-09-19T18:31:41.079385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
186.113 1
 
5.3%
169.977 1
 
5.3%
90.023 1
 
5.3%
129.989 1
 
5.3%
99.607 1
 
5.3%
119.972 1
 
5.3%
148.063 1
 
5.3%
92.004 1
 
5.3%
155.11 1
 
5.3%
138.015 1
 
5.3%
Other values (9) 9
47.4%
ValueCountFrequency (%)
74.995 1
5.3%
75.999 1
5.3%
90.023 1
5.3%
92.004 1
5.3%
99.607 1
5.3%
119.972 1
5.3%
124.031 1
5.3%
129.989 1
5.3%
135.055 1
5.3%
138.015 1
5.3%
ValueCountFrequency (%)
197.964 1
5.3%
195.97 1
5.3%
186.113 1
5.3%
171.931 1
5.3%
169.977 1
5.3%
165.355 1
5.3%
155.11 1
5.3%
148.749 1
5.3%
148.063 1
5.3%
138.015 1
5.3%

duration_ms
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct19
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean228283.58
Minimum144547
Maximum349653
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size284.0 B
2024-09-19T18:31:41.223911image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum144547
5-th percentile159466.3
Q1187873.5
median230142
Q3241915.5
95-th percentile341601.6
Maximum349653
Range205106
Interquartile range (IQR)54042

Descriptive statistics

Standard deviation53894.753
Coefficient of variation (CV)0.23608686
Kurtosis0.78544313
Mean228283.58
Median Absolute Deviation (MAD)28711
Skewness0.82527195
Sum4337388
Variance2.9046444 × 109
MonotonicityNot monotonic
2024-09-19T18:31:41.351211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
176346 1
 
5.3%
202333 1
 
5.3%
258853 1
 
5.3%
234813 1
 
5.3%
180160 1
 
5.3%
340707 1
 
5.3%
236440 1
 
5.3%
161124 1
 
5.3%
180960 1
 
5.3%
194787 1
 
5.3%
Other values (9) 9
47.4%
ValueCountFrequency (%)
144547 1
5.3%
161124 1
5.3%
176346 1
5.3%
180160 1
5.3%
180960 1
5.3%
194787 1
5.3%
202333 1
5.3%
217944 1
5.3%
225916 1
5.3%
230142 1
5.3%
ValueCountFrequency (%)
349653 1
5.3%
340707 1
5.3%
280152 1
5.3%
258853 1
5.3%
244538 1
5.3%
239293 1
5.3%
238680 1
5.3%
236440 1
5.3%
234813 1
5.3%
230142 1
5.3%

genero_clean
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size284.0 B
0
16 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 16
84.2%
3 3
 
15.8%

Length

2024-09-19T18:31:41.521252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-19T18:31:41.644346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 16
84.2%
3 3
 
15.8%

Most occurring characters

ValueCountFrequency (%)
0 16
84.2%
3 3
 
15.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 16
84.2%
3 3
 
15.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 16
84.2%
3 3
 
15.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 16
84.2%
3 3
 
15.8%

Interactions

2024-09-19T18:31:31.401639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:17.702685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:19.179042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:20.357654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:21.613105image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:22.761927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:24.071242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:25.346076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:26.626631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:27.796319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:29.089459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:30.246129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:31.500168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:17.825329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:19.285602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:20.468211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:21.713241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:22.865921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:24.181274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:25.471062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:26.729215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:27.904342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:29.188076image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:30.351213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:31.591216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:17.958864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:19.377617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:20.572824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:21.808738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:22.965981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:24.276884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:25.578620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:26.828304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:27.994890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:29.280151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:30.443740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:31.691763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:18.120908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:19.486164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:20.682404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:21.913825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:23.080560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:24.387962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:25.691198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:26.936331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:28.096942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:29.384701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:30.550824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:31.784258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:18.227951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:19.578702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:20.782910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:21.998895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:23.174670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:24.484506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:25.790219image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:27.032463image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:28.189995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:29.474723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:30.640324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:31.875305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:18.330977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:19.676222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:20.877498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:22.091513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:23.262665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:24.584119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:25.894274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:27.125438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:28.278569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:29.566757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:30.735898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:31.980290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:18.438613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:19.779768image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:20.988101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:22.186586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:23.364719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:24.687227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:26.002285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:27.228020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:28.515698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:29.677277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:30.835398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:32.084837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:18.554161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:19.883317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:21.097676image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:22.288095image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:23.468796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:24.802752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:26.108346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:27.331124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:28.618740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:29.778857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:30.937445image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:32.175913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:18.652159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:19.976894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:21.196310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:22.377644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:23.566916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:24.912292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:26.209919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:27.417633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:28.712276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:29.875404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:31.026476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:32.272959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:18.760233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:20.075450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:21.303862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:22.473748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:23.798510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:25.013807image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:26.307468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:27.513704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:28.803823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:29.969431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:31.125062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:32.365505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:18.858835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:20.175034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:21.410438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:22.572778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:23.893617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:25.125961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:26.415459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:27.611777image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:28.904425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:30.061496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:31.220608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:32.456023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:19.085956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:20.271137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:21.512523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:22.674353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:23.986662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:25.236492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:26.522027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:27.708299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:28.998440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:30.161104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-19T18:31:31.312588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-19T18:31:41.740369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
acousticnessdanceabilityduration_msenergygenero_cleaninstrumentalnesskeylivenessloudnessmodeplaylist_genreplaylist_subgenrespeechinesstempotrack_popularityvalence
acousticness1.0000.518-0.081-0.3330.410-0.3010.385-0.574-0.2540.0000.4100.4910.000-0.3280.2540.245
danceability0.5181.000-0.173-0.4950.6060.0250.240-0.286-0.5000.0000.6060.240-0.426-0.6720.0960.397
duration_ms-0.081-0.1731.000-0.3470.3980.3090.4110.047-0.1740.4100.3980.4320.1020.081-0.070-0.532
energy-0.333-0.495-0.3471.0000.0000.0300.1020.1070.7050.1020.0000.0000.3530.370-0.293-0.018
genero_clean0.4100.6060.3980.0001.0000.0000.2970.0000.0740.0000.7890.9390.2970.0000.0000.297
instrumentalness-0.3010.0250.3090.0300.0001.0000.2040.107-0.2230.1070.0000.000-0.193-0.135-0.221-0.051
key0.3850.2400.4110.1020.2970.2041.000-0.077-0.0670.0000.2970.3650.138-0.198-0.299-0.351
liveness-0.574-0.2860.0470.1070.0000.107-0.0771.0000.0750.0000.0000.0700.0530.116-0.034-0.243
loudness-0.254-0.500-0.1740.7050.074-0.223-0.0670.0751.0000.0000.0740.2080.3180.5610.1020.110
mode0.0000.0000.4100.1020.0000.1070.0000.0000.0001.0000.0000.0000.0000.0000.0000.000
playlist_genre0.4100.6060.3980.0000.7890.0000.2970.0000.0740.0001.0000.9390.2970.0000.0000.297
playlist_subgenre0.4910.2400.4320.0000.9390.0000.3650.0700.2080.0000.9391.0000.5110.0000.0000.291
speechiness0.000-0.4260.1020.3530.297-0.1930.1380.0530.3180.0000.2970.5111.0000.696-0.111-0.111
tempo-0.328-0.6720.0810.3700.000-0.135-0.1980.1160.5610.0000.0000.0000.6961.0000.191-0.004
track_popularity0.2540.096-0.070-0.2930.000-0.221-0.299-0.0340.1020.0000.0000.000-0.1110.1911.0000.361
valence0.2450.397-0.532-0.0180.297-0.051-0.351-0.2430.1100.0000.2970.291-0.111-0.0040.3611.000

Missing values

2024-09-19T18:31:32.753673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-19T18:31:33.103316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

track_idtrack_nametrack_artisttrack_popularitytrack_album_idtrack_album_nametrack_album_release_dateplaylist_nameplaylist_idplaylist_genreplaylist_subgenredanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_msgenero_clean
06nTiIhLmQ3FWhvrGafw2zjAmerican IdiotGreen Day785dN7F9DV0Qg1XRdIgW8rkeAmerican Idiot2004-09-21Dr. Q's Prescription Playlist💊6jAPdgY9XmxC9cgkXAVmVvpoppost-teen pop0.3800.9881-2.04210.06390.0000260.0000790.36800.769186.1131763463
13CRDbSIZ4r5MsZ0YwxuEknStressed OutTwenty One Pilots833cQO7jp5S9qLBoIVtbkSM1Blurryface2015-05-15BALLARE - رقص1CMvQ4Yr5DlYvYzI0Vc2UEpoppost-teen pop0.7340.6374-5.67700.14100.0462000.0000230.06020.648169.9772023333
201vv2AjxgP4uUyb8waYO5YMorphTwenty One Pilots70621cXqrTSSJi1WqDMSLmbLTrench2018-10-05Electropop2Z5cPJ6Z4EVZAfF08amjvLpopelectropop0.7340.6078-7.24900.08060.0747000.0005770.09680.51890.0232588533
37i9763l5SSfOnqZ35VOcfyHeavydirtysoulTwenty One Pilots713cQO7jp5S9qLBoIVtbkSM1Blurryface2015-05-15②⓪①⑨ mixed2bOjjgN1S3Gqd8vSMyafvJrockpermanent wave0.6130.8737-6.37600.04490.0039700.0011100.36700.392129.9892348130
44EchqUKQ3qAQuRNKmeIpnfThe Kids Aren't AlrightThe Offspring762RNTBrSO8U8XjjEj9RVvZ5Americana1998-11-16SNZB PERMANENT WAVE6CgjYkPIWTxJi8RtPcki02rockpermanent wave0.5230.9431-4.20310.03370.0070400.0000380.05790.76699.6071801600
55Ohxk2dO5COHF1krpoPigNSign of the TimesHarry Styles801FZKIm3JVDCxTchXDo5jOVHarry Styles2017-05-12I didn’t know perm stood for permanent (wave)3e6gYPyrTbaB8BWgSHCt5jrockpermanent wave0.5160.5955-4.63010.03130.0275000.0000000.10900.222119.9723407070
65ZLCyAR6Ti5ueOiPGl41XHSleep AloneTwo Door Cinema Club557mxF1FNLd12k1e5MpkMdKgBeacon2012-08-31permanent wave3uFyGoayrP71xS6T6Y8Bh2rockpermanent wave0.5300.7510-5.16510.04320.0001030.0017500.09260.522148.0632364400
7086myS9r57YsLbJpU0TgK9Why'd You Only Call Me When You're High?Arctic Monkeys7678bpIziExqiI9qztvNFlQuAM2013-09-09permanent wave3uFyGoayrP71xS6T6Y8Bh2rockpermanent wave0.6910.6312-6.47810.03680.0483000.0000110.10400.80092.0041611240
86GGpso9iwdxajUMNRlPAm5Darksideblink-182234ZkVdtYgr99rWfRs0fNO4DDarkside2019-07-26Modern Indie Rock // Alternative Rock / Garage Rock / Pop Punk / Grunge / Britpop / Pop Rock1VnvyBDqoV5TCZAnXYferLrockpermanent wave0.4970.9234-3.04010.11000.0062700.0000000.08200.545155.1101809600
95t1sJQEcAltWjCfGL50aQTGraveWage War532tStEaMvl7vkfgAEwysgYPPressure2019-08-30Rock Hard37i9dQZF1DWWJOmJ7nRx0Crockhard rock0.4910.9817-3.67300.13100.0116000.0009710.14400.366138.0151947870
track_idtrack_nametrack_artisttrack_popularitytrack_album_idtrack_album_nametrack_album_release_dateplaylist_nameplaylist_idplaylist_genreplaylist_subgenredanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_msgenero_clean
95t1sJQEcAltWjCfGL50aQTGraveWage War532tStEaMvl7vkfgAEwysgYPPressure2019-08-30Rock Hard37i9dQZF1DWWJOmJ7nRx0Crockhard rock0.4910.9817-3.67300.13100.0116000.0009710.14400.366138.0151947870
106hyKhlwAvLvVh0ZysHFLnqMONOMANIAThe Word Alive542cUtWBZSDdKGEQulGpzxynM0N0MANIA2020-01-10Rock Hard37i9dQZF1DWWJOmJ7nRx0Crockhard rock0.4560.9305-3.59610.18600.0049100.0001910.15900.416148.7492386800
116PcRGZGpM4a3wcaK59fPsZParanoidI Prevail27BReO1xKzOd4fbL7UMbmEQBreaking Down / Bow Down / Paranoid2019-03-18Rock Hard37i9dQZF1DWWJOmJ7nRx0Crockhard rock0.5580.7931-6.86100.03740.0006410.0000290.73100.45775.9991445470
122eZjO58Qe9og2xmyfw4qwJLudensBring Me The Horizon734IojpVltvdlDvlQ6uC6iVWLudens2019-11-06Rock Hard37i9dQZF1DWWJOmJ7nRx0Crockhard rock0.2950.6791-6.18210.15000.0005510.0000540.16500.381197.9642801520
131tS3YIyOAczsBzjub9PrZjSoak Me in BleachThe Amity Affliction616yQXOc0luqwZYOT0jkgrqJSoak Me in Bleach2020-01-08Rock Hard37i9dQZF1DWWJOmJ7nRx0Crockhard rock0.1850.9100-5.28310.15100.0001400.0000000.18000.188165.3552259160
147c8vaoaPChZNEFEPWoAQ4iThe OfferingSleep Token25dSl04wZKKLyf6Z5LvvAkEThe Offering2019-07-04Rock Hard37i9dQZF1DWWJOmJ7nRx0Crockhard rock0.4800.8616-6.83610.04770.0032900.0559000.09070.377135.0553496530
1565nxwwEt3wwNv5hFqUQWphTapping OutIssues502jLAqtOVud3faj6AsgHaAcTapping Out2019-05-03Rock Hard37i9dQZF1DWWJOmJ7nRx0Crockhard rock0.4490.9737-4.60910.18400.0162000.0000020.08280.366195.9702179440
162XAjNdO20yRaXSGARd6fCwChemicalThe Devil Wears Prada474pEnCDCsZ8iWC3HaF98g2PChemical2019-09-27Rock Hard37i9dQZF1DWWJOmJ7nRx0Crockhard rock0.5260.7895-7.34200.03330.0022000.0175000.08500.20974.9952301420
172gYCaUsqrnBOt7DymQCY6dLegendarySkillet143YBM8Bk5cfjLLTwJx8uWXbLegendary2019-05-08Rock Hard37i9dQZF1DWWJOmJ7nRx0Crockhard rock0.5480.92411-4.48000.06890.0003300.0000140.16200.242124.0312445380
184Smc57rqnTjKvz56WJaunvHappy SongBring Me The Horizon586XPW94L30lADaLwczUnLFhThat's The Spirit (Track by Track Commentary)2015-09-04Workout Hard Rock4TG1lzMD9HFvZ9E1Bk6Gnurockhard rock0.2960.9125-3.43310.06450.0000340.0026400.38900.276171.9312392930